Context models are employed in a wide variety of different computational applications, including data compression, data prediction algorithms, and methods for denoising discrete digital data, including text data, image data, and other types of data that is stored in electronic media and/or transmitted to remote computers via electronic communications media. In certain applications, fixed length symbol contexts preceding, following, or both preceding and following each symbol in a data stream are considered in order to interpret the symbol. For example, in discrete digital data denoising, preceding and following contexts allow a denoising process to recognize and ameliorate data corruption arising from noise-introducing storage and retrieval and/or noise-introducing transmission. It is conceptually and programmatically easy to consider fixed-length contexts preceding and following a symbol. However, when the occurrences of different symbols within a data stream are non-uniform, it may be advantageous to employ variable-length contexts. Computer scientists, information-theory researchers, and designers, developers, and manufacturers of various computational systems have recognized the need for computationally straightforward and efficient methods for determining, representing, and using variable-length contexts in a wide variety of computational applications.